The present disclosure relates to the image analysis arts, traffic enforcement arts, image modification arts, privacy protection arts, facial recognition arts, and more particularly to filtering or redaction of faces in windshield images.
In order to manage ever-increasing traffic numbers, special lanes are introduced that allow only traffic with more than a certain number of occupants inside a vehicle. These managed lanes include carpool, diamond, or High Occupancy Vehicle (HOV) lanes that are intended to reduce the total number of cars (for a given number of people) on the highway and thus to speed up travel. The overall benefits are obvious in multiple areas: the managed lanes reduce traffic congestion, reduce wasted commuting time, reduce fuel consumption, and decrease pollution. Managed lanes also include High Occupancy Tolling (HOT) lanes where a single occupant vehicle can use the managed lane upon payment of a toll. The toll is often dynamically set based on real-time traffic congestion to maintain a minimum average vehicle speed in the managed lane. Managed lanes, such as the aforementioned HOV or HOT lanes, are typically the left most lanes of a highway and are often denoted by diamond markings on the pavement within the lanes and/or signage. Sometimes they are separated from the general-purpose lanes using barriers. Some managed lanes require at least two vehicle occupants, denoted as a “2+” lane, and other managed lanes require at least three vehicle occupants, denoted as a “3+” lane.
In order to be effective and maintain integrity within the system, adherence to the occupancy numbers has to be enforced. Since managed lanes generally give a clear advantage in terms of travel time, people are tempted to cheat the system and use the lane even if their vehicle does not carry the sufficient number of occupants (or is otherwise ineligible) required. This tendency to cheat sometimes also includes efforts to avoid detection, including the use of dummies or mannequins to simulate additional occupants.
To enforce the rules of managed lanes, current practice requires dispatching law enforcement officers at the side of HOV/HOT lanes to visually examine passing vehicles. This method is expensive, difficult, potentially unsafe, and ultimately ineffective as few violators are actually caught and ticketed. An alternate method of monitoring managed lanes is image-based automatic enforcement which requires identification and classification of image features (e.g., faces, seats, seat belts, etc.) behind a windshield that are visible to the camera to distinguish a driver+passenger configuration vs. a driver only configuration. This method may be dependent upon camera placement and trigger timing to obtain a clear image of the interior of a vehicle. In most locations, it is not possible to aim the camera such that its field of view is tightly focused on only the windshield of all oncoming cars. The location of the windshield in captured images will vary from car to car depending on driver behavior and vehicle design, thus reducing the effectiveness of such an image based approached. As such, accurate localization of the windshield region from a captured image is required to identify violators in managed lanes.
One approach for identifying the location of a windshield region is set forth in commonly assigned U.S. Pat. No. 8,971,579 B2, issued Mar. 3, 2015 wherein a target vehicle within a captured image can be identified and localized based on prior knowledge of geometric and spatial relationships. Objects of interest on the target vehicle can then be identified and utilizing a priori knowledge of the relative geometric relationships between the identified objects, the area of the image containing the windshield of the target vehicle can be identified and localized for downstream processing to detect vehicles in violation of HOV/HOT lane requirements or other violations, such as seat belt requirements.
Currently, however, the law enforcement officers enforce the day-to-day overseeing of managed lane regulations by manual observations. Unfortunately, current practice is expensive and more importantly, ineffective. The aforementioned automatic camera (image-based) approach replaces this manual enforcement approach. Certain issues arise when using image capturing enforcement tools, primarily fulfilling privacy requirements of drivers and occupants. During image-based enforcement, there is a need to redact the face regions in the collected images to respect the privacy of the commuters. Concurrent with the redaction of faces from the images, however, is the need for the images to have enough detail preserved so that a person viewing the images can easily count the number of people within the vehicle. Law enforcement need to be able to count the number of people and the violator needs to be provided with imagery documenting their violation.
Contemporaneous facial redaction methodologies in the aforementioned camera system are typically based on face detection algorithms. Initially, region of interest (i.e., face) is detected using one of the state-of-the-art face detection techniques, and then the face region is redacted accordingly. Unfortunately, face detection based redaction techniques can perform poorly for arbitrary viewing angles, facial pose variation, occluded and partially occluded faces, poor contrast and low-resolution imagery.
Accordingly, there remains a need for an efficient, fast, and inexpensive facial redaction method allowing for the display of front view camera images anonymously.